Short-term forecasting method and device for large floating algae

Abstract

The invention discloses a large floating algae short-term forecasting method and device, and the method comprises the steps: inputting physical field data into an FVCOM hydrodynamic forecasting model, and obtaining a hydrodynamic mode forecasting result; obtaining a remote sensing monitoring result of the day through remote sensing monitoring; inputting the hydrodynamic mode prediction result and the remote sensing monitoring result into a large floating algae growth and drifting model to obtain a short-term prediction result; and post-processing the short-term prediction result to obtain an algae prediction result. The method can improve the accuracy of floating algae forecasting, and can be widely applied to the technical field of algae forecasting.

Description

Technical Field

[0001] The invention relates to the technical field of algae forecasting, in particular to a method and a device for short-term forecasting of large floating algae. Background Art

[0002] At present, the forecasting technology for large floating algae disasters mainly adopts the simple 48-72 hour forecast of floating algae transport trajectory achieved by hydrodynamic and wind-driven particle tracking. This forecasting technology has a single function and a short effective forecast time. In addition, due to the biological characteristics of large floating algae that multiply and disappear quickly, the single trajectory prediction accuracy is low, and it is impossible to accurately describe the growth and transport process of floating algae, making the forecast of floating algae inaccurate. Summary of the invention

[0003] In view of this, the main purpose of the embodiments of the present invention is to provide a method and device for short-term forecasting of large floating algae, in order to solve at least one problem of the prior art. The present invention can improve the accuracy of floating algae forecasting.

[0004] To achieve the above object, an embodiment of the present invention provides a short-term forecasting method for large floating algae, the method comprising the following steps:

[0005] Input the physical field data into the FVCOM hydrodynamic prediction model to obtain the hydrodynamic pattern prediction results;

[0006] Through remote sensing monitoring, obtain the remote sensing monitoring results of the day;

[0007] Inputting the hydrodynamic model prediction results and the remote sensing monitoring results into a large floating algae growth and drift model to obtain a short-term prediction result;

[0008] The short-term prediction results are post-processed to obtain algae forecast results.

[0009] In some embodiments, the step of inputting the hydrodynamic model prediction results and the remote sensing monitoring results into a large floating algae growth and drift model to obtain a short-term prediction result comprises the following steps:

[0010] According to the prediction results of the hydrodynamic model, Lagrangian particle tracing is performed through a particle tracing module to obtain the physical transport process;

[0011] According to the hydrodynamic model prediction results and the remote sensing monitoring results, the particles are replicated and merged through the algae growth module to obtain the dynamic growth process;

[0012] The physical transport process and the dynamic growth process are coupled to obtain a short-term prediction result;

[0013] Wherein, the large floating algae growth and drift model includes the particle tracing module and the algae growth module.

[0014] In some embodiments, performing Lagrangian particle tracing through a particle tracing module according to the hydrodynamic model prediction result to obtain the physical transport process includes the following steps:

[0015] Performing time interpolation and space interpolation operations on the prediction results of the hydrodynamic model to obtain the ocean current velocity;

[0016] Obtain sea surface wind forcing through meteorological forecast models;

[0017] Set the wind drag coefficient according to the floating characteristics;

[0018] According to the unit motion speed in the random direction, the random diffusion coefficient in the horizontal direction and the time step, the random motion in the horizontal direction is obtained;

[0019] Based on the fourth-order Runge-Kutta method, the Lagrangian particle drift trajectory is obtained according to the ocean current velocity, the sea surface wind forcing, the wind drag coefficient and the random motion in the horizontal direction;

[0020] The ocean current and sea surface wind in the prediction results of the hydrodynamic model are used as physical drivers of the particles;

[0021] The physical transport process is obtained according to the physical drive and the Lagrangian particle drift trajectory.

[0022] In some embodiments, the expression of the Lagrangian particle drift trajectory is:

[0023]

[0024] Where, X t+Δt represents the horizontal position of the particle at time t+Δt; X t represents the horizontal position of the particle at time t; represents the particle x at time t t Velocity vector in the horizontal direction; Represents the random motion of particles in the horizontal direction within the time step Δt of particle physics motion calculation; x t represents a particle; t represents time; Δt represents the time step for calculating the particle's physical motion.

[0025] In some embodiments, the process of replicating and merging particles through an algae growth module according to the hydrodynamic model prediction results and the remote sensing monitoring results to obtain a dynamic growth process includes the following steps:

[0026] Preset initial biomass;

[0027] Get the current first biomass of a single particle;

[0028] replicating the single particle according to the current first biomass of the single particle and the initial biomass;

[0029] Merging the single particle and the neighboring particles according to the current first biomass of the single particle, the initial biomass, and the current second biomass of neighboring particles of the single particle;

[0030] According to the operations of copying and merging, the dynamic growth process is obtained.

[0031] In some embodiments, the replicating the single particle according to the current first biomass of the single particle and the initial biomass comprises the following steps:

[0032] When the current first biomass of the single particle is greater than twice the initial biomass, a new particle is randomly copied and placed within a preset range of the single particle, and the current first biomass of the single particle and the current third biomass of the new particle are both set to the initial biomass.

[0033] In some embodiments, the merging of the single particle and the neighboring particles according to the current first biomass of the single particle, the initial biomass, and the current second biomass of the neighboring particles of the single particle comprises the following steps:

[0034] When the current first biomass of the single particle and the current second biomass of the neighboring particles of the single particle are both less than half of the initial biomass, the single particle and the neighboring particles are merged to obtain a merged particle, and the current fourth biomass of the merged particle is set as the initial biomass.

[0035] To achieve the above object, another aspect of the embodiment of the present invention provides a short-term forecasting device for large floating algae, the device comprising:

[0036] The first module is used to input the physical field data into the FVCOM hydrodynamic prediction model to obtain the hydrodynamic model prediction results;

[0037] The second module is used to obtain the remote sensing monitoring results of the day through remote sensing monitoring;

[0038] The third module is used to input the hydrodynamic model prediction results and the remote sensing monitoring results into the large floating algae growth and drift model to obtain short-term prediction results;

[0039] The fourth module is used to post-process the short-term prediction results to obtain algae forecast results.

[0040] To achieve the above objective, another aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the aforementioned large floating algae short-term forecasting method when executing the computer program.

[0041] To achieve the above objective, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for short-term forecasting of large floating algae described above is implemented.

[0042] To achieve the above object, another aspect of the embodiments of the present invention provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aforementioned large floating algae short-term forecasting method.

[0043] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method and device for short-term forecasting of large floating algae, which obtains hydrodynamic pattern prediction results by inputting physical field data into the FVCOM hydrodynamic forecasting model; obtains the remote sensing monitoring results of the day through remote sensing monitoring; inputs the hydrodynamic pattern prediction results and the remote sensing monitoring results into the large floating algae growth and drift model to obtain short-term prediction results; and post-processes the short-term prediction results to obtain algae forecast results. The embodiments of the present invention solve the problems of the forecasting links in the forecasting of algae disasters, such as the simple forecasting model of the scale of algae outbreaks, the difficulty in quantifying the forecast of the biomass and coverage of algae outbreaks, and the insufficient reliability of the algae transport trajectory, through the large floating algae growth and drift model, supported by physical field data and remote sensing monitoring data. The present invention can improve the accuracy of floating algae forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 is a flow chart of a short-term forecasting method for large floating algae provided by an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the framework of a short-term forecast system for large floating algae provided by an embodiment of the present invention;

[0047] Figure 3 Schematic diagram of the framework of the large floating algae growth and drift prediction model provided by the embodiment of the present invention;

[0048] Figure 4a-4b is a schematic diagram of the replication and merging process of simulated algae particles provided by an embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram comparing the curved grid and the triangular grid in shoreline fitting provided by an embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of σ-s hybrid coordinates provided by an embodiment of the present invention;

[0051] Figure 7 is a schematic diagram of FVCOM grid configuration provided by an embodiment of the present invention;

[0052] Figure 8 is a schematic diagram of a Jiangsu offshore model architecture provided by an embodiment of the present invention;

[0053] Fig. 9 is a schematic diagram of a calculation area (grid) provided in an embodiment of the present invention;

[0054] Fig.10 is a schematic diagram of a grid resolution matrix for offshore Jiangsu provided by an embodiment of the present invention;

[0055] Fig.11 is a schematic diagram of an unstructured grid in the Jiangsu sea area provided by an embodiment of the present invention;

[0056] Figure 12a-12b is a schematic diagram of a short-term forecast product for the Yellow Sea green tide in 2022 provided by an embodiment of the present invention;

[0057] Fig.13 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention, and they are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0059] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the flow chart. The terms "first / S100" and "second / S200" in the specification and claims and the above-mentioned drawings may be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiment of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0060] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0062] Before describing the embodiments of the present invention in detail, some nouns and terms involved in the embodiments of the present invention are first described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0063] Large floating algae refer to large algae plants that float and grow on the water surface throughout their entire life cycle or half of their life cycle.

[0064] FVCOM (Finite-Volume Coastal Ocean model) is a comprehensive ocean numerical model that uses unstructured grids and integrates modules such as hydrodynamics, sediment transport and landform evolution, water quality, ecology, and sea ice.

[0065] The full name of the European Centre for Medium-Range Weather Forecasts (ECMWF) is an important organization focusing on global weather forecasting and climate monitoring. Through advanced technology and international cooperation, it provides important support and leadership for weather forecasting, climate monitoring and scientific research.

[0066] The Global Forecast System (GFS) is a numerical forecast model system that plays an important role in weather and climate forecasting. Through efficient numerical simulation and data analysis, it provides valuable scientific support and decision-making basis for global meteorological and environmental management.

[0067] TPXO is an ocean tide model that is mainly used to predict and simulate tidal phenomena in the global ocean. It combines satellite observation data and numerical models to predict tidal changes in various regions of the earth, and has important application value in marine engineering, navigation and other fields.

[0068] HYCOM (HYbrid Coordinate Ocean Model) is an ocean circulation model that uses a hybrid coordinate system to simulate and predict the three-dimensional flow field and temperature distribution of the global ocean. It combines observational data, numerical methods and physical models to study issues such as ocean circulation, climate change, and marine ecology.

[0069] RADAR (RAdio Detection And Ranging) is a technology that detects targets by transmitting radio waves and receiving their reflected signals. In earth science, RADAR is often used for remote sensing and measuring the characteristics and movement of targets such as the atmosphere, surface, and glaciers to obtain high-resolution spatial data.

[0070] MEaSUREs (Measurements of Earth System Data Records) is a NASA project that aims to establish and maintain Earth system data records. These data records are based on remote sensing, satellite observations, and model outputs, and are used to analyze changes in the Earth system, climate trends, surface changes, etc., to provide support for global environmental and climate research.

[0071] Dissolved inorganic nitrogen includes nitrogen compounds in water in inorganic form, mainly nitrite, nitrate and ammonia nitrogen, etc. These inorganic nitrogen compounds are usually the main nitrogen sources available to organisms, and have an important impact on the growth of aquatic organisms and the nutritional status of the ecosystem.

[0072] Dissolved inorganic phosphorus refers to phosphorus compounds that exist in water in inorganic form, mainly orthophosphates, phosphites, etc. Dissolved inorganic phosphorus is another key nutrient in water, which plays an important role in the growth of algae and other aquatic organisms and the health of the ecosystem.

[0073] The current forecasting technology for large floating algae disasters has a single function and a short effective forecast time. In addition, due to the biological characteristics of large floating algae that multiply and disappear rapidly, the single trajectory prediction has low accuracy and cannot accurately depict the growth and transport process of floating algae.

[0074] In view of this, if Figure 1 As shown, an embodiment of the present invention provides a short-term forecasting method for large floating algae, and the method may include but is not limited to steps S100 to S400:

[0075] Step S100, inputting the physical field data into the FVCOM hydrodynamic prediction model to obtain the hydrodynamic mode prediction result;

[0076] Step S200, obtaining remote sensing monitoring results of the day through remote sensing monitoring;

[0077] Step S300, inputting the hydrodynamic model prediction result and the remote sensing monitoring result into a large floating algae growth and drift model to obtain a short-term prediction result;

[0078] Step S400, post-processing the short-term prediction result to obtain an algae forecast result.

[0079] In steps S100 to S400 of some embodiments, the latest physical field data and remote sensing monitoring data of the day are automatically identified and acquired, and the multi-source remote sensing monitoring data existing and acquired on the day are merged to improve the quality of monitoring data; automatic operation is achieved in a series of processes from identifying and acquiring data, data standardization processing to starting the forecast simulation. Through the FVCOM hydrodynamic forecast model and the growth and drift model of large floating algae, short-term forecasts of regional marine hydrodynamic environment and algae disaster outbreaks are carried out with the support of physical field data and remote sensing monitoring data. The forecast results will be automatically processed into forecast chart products such as coverage, biomass, transport trajectory, stranded location and stranded biomass for publication. It solves the problems of the forecast link in the forecast of algae disasters, such as the simple forecast model of the scale of algae outbreaks, the difficulty in quantifying the forecast of algae outbreak biomass and coverage, and the lack of reliability of algae transport trajectory.

[0080] In some embodiments, when there are outbreaks of large floating algae of different species in the same area, the large floating algae growth and drift model can set up and start dynamic growth modules for multiple floating algae, and during the forecast simulation process, call the dynamic growth calculation modules for different algae in the model for the different floating algae initially set up, so as to realize the forecast of the joint outbreak of multiple algae.

[0081] In step S100 of some embodiments, Figure 2As shown in the figure, by taking the tidal oscillation boundary, initial temperature-salinity field, thermal radiation flux and sea surface wind forcing data as input data and inputting them into the FVCOM hydrodynamic forecast model, we can get the hydrodynamic pattern prediction results for the first 7 days, including but not limited to nutrients, radiation intensity, temperature-salinity, ocean currents, wind fields, etc.

[0082] In step S200 of some embodiments, the business-oriented automatic forecast includes automated determination and acquisition of remote sensing monitoring, physical field and other data. If the forecast for the day includes remote sensing monitoring data from multiple sources, such as multi-source satellite remote sensing monitoring data, the multi-source remote sensing monitoring data are merged and processed to complement each other. The monitoring coverage range and coverage area results are extracted to determine the number of simulated particles and the initial distribution position of the particles. The extracted coverage range and coverage area results are standardized to obtain the initialization particle distribution file required for the day's algal bloom disaster forecast, which can be made into initialization data for the day's forecast simulation.

[0083] In some embodiments, step S300 may include but is not limited to steps S310 to S330:

[0084] Step S310, performing Lagrangian particle tracing through a particle tracing module according to the hydrodynamic model prediction result to obtain a physical transport process;

[0085] Step S320, according to the hydrodynamic model prediction result and the remote sensing monitoring result, the algae growth module is used to replicate and merge particles to obtain a dynamic growth process;

[0086] Step S330, coupling the physical transport process and the dynamic growth process to obtain a short-term prediction result;

[0087] In steps S310 to S330 of some embodiments, the hydrodynamic model prediction results are input into the large floating algae growth and drift model as environmental driving factors, and the remote sensing monitoring results of the day are input into the large floating algae growth and drift model. Through Lagrangian particle tracing and particle replication and merging, the physical-biological process of the algae disaster outbreak is well reproduced, thereby realizing the short-term prediction of the outbreak scale, impact area and biomass of the large floating algae outbreak process, and meeting the business needs of green tide forecasting and early warning. In the dynamic growth process of floating algae when floating algae float freely, the growth stage and aggregation density of the simulated algae can be used as options, and the predicted large floating algae transport characteristics are fed back to the physical transport process and act on the calculation of the transport speed; the changes in the spatial position and external environmental conditions of the algae caused by the simulation of the physical transport calculation of the algae can be updated in real time and act on the dynamic growth process calculation. The floating growth and transport process of large floating algae in the natural environment can be better reproduced through the two-way coupling of the physical transport process and the dynamic growth process.

[0088] In some embodiments, Figure 3 As shown, the large floating algae growth and drift model includes a particle tracking module and an algae growth module. Exemplarily, based on the FVCOM v4.3 offline Lagrangian particle tracking module, a single particle is used as an independent simulation calculation unit, and the surface current and sea surface wind are used as physical forcings in the floating state to achieve tracking and forecasting simulation of the spatial position of large floating algae. In addition, in the algae growth module, according to the environmental factors of the particle's spatiotemporal position, including surface water temperature, sea surface light radiation intensity, and nutrient concentration, physiological processes such as biological photorespiration and dark respiration are simulated and calculated, and the dynamic growth and extinction process of algae is simulated through the replication and merging of particles. Among them, the determination of the growth and extinction rate depends on the full study of the life history of the algae in the floating state. Large floating algae are regarded as independent individual particles, and their dynamic growth process is presented through the proliferation and reduction of particles. The physical transport process of large floating algae is dynamically coupled with the dynamic growth process. As the model simulation proceeds, the particles proliferate or decrease and are diffused and transported with the ocean currents. Due to the differences in external environmental factors in which the particles are located, their growth process changes dynamically, thereby achieving dynamic simulation of the distribution range of large floating algae outbreaks and biomass changes.

[0089] In some embodiments, in the growth and drift model of large floating algae, data on marine environmental elements such as surface currents, surface water temperature, sea surface light radiation intensity, and nutrient concentration can be obtained through the high-precision regional ocean numerical model provided by the forecast center, and nutrient distribution data can be obtained through the digitized nautical chart of the corresponding sea area, and sea surface wind data can be obtained through ECMWF or GFS.

[0090] In step S310 of some embodiments, Lagrangian particle tracing is performed through a particle tracing module based on the hydrodynamic model prediction results, wherein the physical drive calculation in the tracing module is driven by surface currents and sea winds, the wind drag coefficient calculated for large floating algae with different morphological characteristics is adjustable, and the physical transport driving coefficient dynamically changes according to the growth stage of the algae and the degree of algae aggregation, so as to obtain the physical transport process.

[0091] In some embodiments, step S310 may include but is not limited to steps S311 to S317:

[0092] Step S311, performing time interpolation and space interpolation operations on the hydrodynamic model prediction result to obtain the ocean current velocity;

[0093] Step S312, obtaining sea surface wind forcing through a meteorological forecast model;

[0094] Step S313, setting the wind drag coefficient according to the floating characteristics;

[0095] Step S314, obtaining random motion in the horizontal direction according to the unit motion speed in the random direction, the random diffusion coefficient in the horizontal direction, and the time step;

[0096] Step S315, based on the fourth-order Runge-Kutta method, obtain the Lagrangian particle drift trajectory according to the ocean current velocity, the sea surface wind forcing, the wind drag coefficient and the random motion in the horizontal direction;

[0097] Step S316, using the ocean current and sea surface wind in the hydrodynamic model prediction result as the physical drive of the particles;

[0098] Step S317, obtaining the physical transport process according to the physical drive and the Lagrangian particle drift trajectory.

[0099] In some embodiments, in steps S311 to S317, in the Lagrangian particle tracing module, the surface current and the sea surface wind are used as the driving force of the particle movement. According to the driving force and the obtained Lagrangian particle drift trajectory, the physical transport process can be obtained. The hydrodynamic model prediction results are interpolated in time and space to obtain the current velocity. The bilinear difference method is used in the vertical and horizontal directions. The sea surface wind forcing is obtained through meteorological forecast models, such as ECMWF or GFS. Among them, the driving effect of sea surface wind forcing on sea surface floating algae is related to the wind drag coefficient κ, which depends on the shape and size of the floating algae and the floating water depth. The wind drag coefficient κ can be set according to the floating characteristics of the floating algae. For example, according to the floating characteristics of the Yellow Sea Enteromorpha, the wind drag coefficient κ can be set to 1%. According to the current speed, sea surface wind forcing and wind drag coefficient, the following expression can be obtained:

[0100]

[0101] In the formula, Represents the particle's velocity vector in the horizontal direction; represents the speed of the ocean current; represents the sea surface wind forcing; κ represents the wind drag coefficient.

[0102] At the same time, the random motion of particles caused by factors other than wind and current forcing during the drift process is considered. Since algae do not have significant vertical motion on the sea surface during their floating growth, only their random motion in the horizontal direction is considered in the model calculation. Then there are the following expressions:

[0103]

[0104] In the formula, represents random motion in the horizontal direction; Δt r Represents the unit time step of random motion calculation; Represents the unit movement speed in a random direction; R represents a random value in the range of 0 to 1.0; K r Represents the random diffusion coefficient in the horizontal direction, in m 2 For example, the random motion calculation time step can be set to 6 seconds, and the random diffusion coefficient in the horizontal direction can be set to 200 according to the floating characteristics of Enteromorpha in the Yellow Sea.

[0105] In some embodiments, based on the fourth-order Runge-Kutta method, the Lagrangian particle drift trajectory can be obtained according to the ocean current velocity, the sea surface wind forcing, the wind drag coefficient, and the random motion in the horizontal direction, wherein the particle physics motion calculation time step can be set to 60 seconds, and the random motion calculation time step is set to 6 seconds. Then, 10 random motion iterative calculations are performed in a single particle physics motion calculation, and the random motion amount in the horizontal direction is expressed as The expression of the Lagrangian particle drift trajectory is:

[0106]

[0107] In the formula, X t+Δt represents the horizontal position of the particle at time t+Δt; t t represents the horizontal position of the particle at time t; represents the particle x at time t t Velocity vector in the horizontal direction; Represents the random motion of particles in the horizontal direction within the time step Δt of particle physics motion calculation; x t represents a particle; t represents time; Δt represents the time step for calculating the particle's physical motion.

[0108] In some embodiments, for the growth calculation of large floating algae with different biological morphological characteristics, the growth and loss of plant volume (leaf area) or the replication and merging of simulated particles can be used to achieve more accurate calculation of the dynamic growth process. These two methods are respectively suitable for the growth simulation of large-sized plant algae represented by brown algae and small-sized plant algae represented by green algae.

[0109] For example, the growth and loss of plant volume (leaf area) is adopted, and a single particle is regarded as a single plant body with volume parameters, and the growth stage of the algae body is determined by the volume parameters (leaf area). 0 It is the growth limiting parameter of plant growth scale, representing the theoretical maximum volume that the plant can grow to when the leaf area is between 50% and 80% A 0When the plant is considered to be in the reproductive period, there is a probability K of reproducing new plants. pro The germination of new plants requires the temperature to be in the suitable growth range, and the initial leaf area of ​​the new germination plants is A init The settings are based on the specific algae species. The plant's demise in this example is determined by plant necrosis and leaf loss.

[0110] In some embodiments, step S320 may include but is not limited to steps S321 to S325:

[0111] Step S321, presetting the initial biomass;

[0112] Step S322, obtaining the current first biomass of a single particle;

[0113] Step S323, replicating the single particle according to the current first biomass of the single particle and the initial biomass;

[0114] Step S324, merging the single particle and the neighboring particles according to the current first biomass of the single particle, the initial biomass, and the current second biomass of the neighboring particles of the single particle;

[0115] Step S325, obtaining the dynamic growth process according to the copying and merging operations.

[0116] In some embodiments, in steps S321 to S325, the algae growth module can reflect the growth and extinction process of the algae in the floating state by copying and merging particles. For example, an initial biomass is preset, and the initial biomass can be adjusted according to the simulation requirements. The biomass of the floating algae block represented by a single initial simulation particle is initialized to the initial biomass m 0 In the process of growth simulation, the current first biomass of a single particle is obtained, and the single particle is copied according to the preset initial biomass and the current first biomass of the single particle; the single particle and the neighboring particles are merged according to the preset initial biomass, the current first biomass of the single particle and the current second biomass of the neighboring particles of the single particle. According to the above-mentioned duplication and merging process, a dynamic growth process can be obtained.

[0117] In step S323 of some embodiments, Figure 4a As shown in FIG. 1 , when the current first biomass of the single particle is greater than twice the initial biomass, a new particle is randomly copied and placed within the preset range of the single particle, and the current first biomass of the single particle and the current third biomass of the new particle are both set to the initial biomass. For example, when the biomass represented by a single particle exceeds 2 times m 0When , a new particle is randomly placed within 2 km of the location of the single particle, and the biomass of both particles are reset to the initial biomass m 0 , which represents the growth and reproduction process of algae.

[0118] In step S324 of some embodiments, Figure 4b As shown in FIG. 1 , when the current first biomass of the single particle and the current second biomass of the neighboring particle of the single particle are both less than half of the initial biomass, the single particle and the neighboring particle are merged to obtain a merged particle, and the current fourth biomass of the merged particle is set to the initial biomass. For example, when the biomass of two simulated particles with similar distances are both less than 0.5 times m 0 When , they are merged into a merged particle with a biomass size equal to the preset initial biomass size. This process represents the extinction process of the algae.

[0119] In some optional embodiments, the internal nitrogen concentration index can also be added to determine the nitrogen-carbon ratio, thereby reflecting the dynamic growth process of the algae. If the current nitrogen-carbon ratio is greater than twice the minimum nitrogen-carbon ratio, it means that the algae is in a suitable reproduction stage; if the current nitrogen-carbon ratio is less than the minimum nitrogen-carbon ratio, it means that the algae has aged or is in an imbalanced state of internal nutrition, losing activity and sinking to die. Exemplarily, the initial biomass and initial internal nitrogen and phosphorus content are preset, and the initial biomass and initial internal nitrogen and phosphorus content can be adjusted according to the simulation requirements, then the biomass of the floating algae block represented by a single initial simulation particle is initialized to the initial biomass m 0 , the internal nitrogen and phosphorus contents are initialized to the initial nitrogen content N 0 and the initial phosphorus content P 0 In the process of growth simulation, the current first biomass and the first nitrogen content of a single particle are obtained, and the single particle is copied according to the preset initial biomass and the current first biomass and the first nitrogen content of the single particle; the single particle and the neighboring particles are merged according to the preset initial biomass, the current first biomass and the first nitrogen content of the single particle, and the current second biomass and the second nitrogen content of the neighboring particles of the single particle. According to the above-mentioned duplication and merging process, a dynamic growth process can be obtained.

[0120] In some optional embodiments, when the current first biomass of the single particle is greater than twice the initial biomass, and the nitrogen-carbon ratio (Q NWhen the nitrogen concentration index representing the internal algae, i.e., the nitrogen-carbon ratio, which can be expressed as N:C) is greater than twice the minimum nitrogen-carbon ratio, a new particle is randomly copied and placed within the preset range of the single particle, and the current first biomass of the single particle and the current third biomass of the new particle are both set to the initial biomass, and the current internal nitrogen-carbon ratio of the new particle is consistent with the nitrogen-carbon ratio of the single particle. For example, when the biomass represented by a single particle exceeds 2 times m 0 , and the internal nitrogen-carbon ratio Q N More than 2 times Q Nmin When , a new particle is randomly placed within the preset range of the location of the single particle, and the biomass of both particles are reset to the initial biomass m 0 , internal nitrogen-carbon ratio Q N Remaining unchanged, this process represents the growth and reproduction process of the algae.

[0121] In some optional embodiments, when the current first biomass of the single particle and the current second biomass of the neighboring particle of the single particle are both less than half of the initial biomass, the single particle and the neighboring particle are merged to obtain a merged particle, and the current fourth biomass of the merged particle is set as the initial biomass; at the same time, when the nitrogen-carbon ratio corresponding to the current first nitrogen content of the single particle is less than the minimum nitrogen-carbon ratio, the first biomass of the particle is reset to zero and removed from the dynamic growth calculation process. Exemplarily, when the biomass of two simulated particles with similar distances are both less than 0.5 times m 0 When the nitrogen-carbon ratio Q of the simulated particle appears, it will be merged into a merged particle with a biomass size equal to the preset initial biomass size. N Less than the minimum nitrogen-carbon ratio Q Nmin When , the particle biomass returns to zero. These two processes represent the extinction process of the algae.

[0122] In some embodiments, the dynamic growth process of algae can be presented by the absorption and consumption of carbon (C), nitrogen (N) and phosphorus (P) by the algae. The following formula is used to calculate the dynamic changes of carbon, nitrogen and phosphorus in the algae:

[0123]

[0124] Where V c Represents the rate of carbon absorption by algae; V N Represents the rate of nitrogen absorption by algae; V P Represents the rate of phosphorus absorption by algae; L c Represents the consumption rate of carbon inside the algae; L N Represents the nitrogen consumption rate inside the algae; L P Represents the consumption rate of phosphorus inside the algae.

[0125] The algae biomass is represented by the carbon content in the algae. The conversion formula between the wet weight of the algae and the carbon content is as follows:

[0126]

[0127] In the formula, FW represents the wet weight of algae; C represents the carbon content; K TOC Representing the conversion coefficient between the wet weight of the algae and the carbon content, illustratively, based on the research results of the physiological characteristics of Enteromorpha in the Yellow Sea, the conversion coefficient is determined to be 8mmolC / gFW.

[0128] In some embodiments, the total biomass of the simulated floating algae is the sum of the wet weights of the biomass of all simulated particles, which can be expressed as follows:

[0129]

[0130] Where M t represents the total simulated algae biomass at time t; N t Represents the total number of simulated particles at time t; FWt t,n Represents the wet weight of the nth algal body at time t.

[0131] The consumption rate of carbon, nitrogen and phosphorus in the algae depends on the photosynthesis, respiration and natural extinction process of the algae, and is ultimately reflected in the change of the algae biomass. c Depends on the photorespiration function f(I), and is limited by the temperature limit function f of photosynthesis p (T) and nutrient limitation function f(N), as well as light shading limitation function f(ρ) due to algal aggregation growth, the following expressions are available:

[0132] V c =f(I)f p (T)f(N)f(ρ)·C (9)

[0133] The photorespiration function f(I) represents the relationship between photosynthesis efficiency and light radiation intensity I, and has the following expression:

[0134]

[0135] Where P max represents the maximum photosynthetic rate; α represents the photosynthetic efficiency of the plant; tanh(·) represents the hyperbolic tangent function.

[0136] The nutrient limitation function f(N) represents the correlation between the nutrient index inside the algae and biological photosynthesis, and has the following expression:

[0137]

[0138] In the formula, Q N Represents the nitrogen concentration index inside the algae, that is, the nitrogen-carbon ratio, which can be expressed as N:C; Q Nmin Represents the minimum nitrogen index of the algae; Q P Represents the phosphorus concentration index inside the algae, that is, the phosphorus-carbon ratio, which can be expressed as P:C; Q Pmin Represents the lowest phosphorus index of algae. Among them, the nitrogen-carbon ratio and phosphorus-carbon ratio have a significant impact on the photosynthesis process of organisms.

[0139] The absorption rate of nitrogen and phosphorus nutrients by algae is highly correlated with the nutrient concentration in the external water body and the nitrogen and phosphorus concentration in the organism. The absorption of nutrients by algae is mainly based on DIN and DIP, which is limited by the temperature limit function f of photosynthesis. p (T) and the light shielding limitation function f(ρ) caused by aggregate growth. The calculation process of the algae's absorption rate of DIN and DIP is:

[0140]

[0141] Where V DIN Represents the absorption rate of DIN by algae; V mDIN Represents the maximum absorption rate of DIN by algae; C DIN Represents the concentration of DIN in water; K DIN Represents the half-saturation coefficient of DIN; Q Nmax Represents the maximum nitrogen concentration index inside the algae; V DIP Represents the absorption rate of DIP by algae; V mDIP Represents the maximum absorption rate of DIP by algae; C DIP Represents the concentration of DIP in water; K DIP represents the half-saturation coefficient of DIP; Q Pmax Represents the maximum phosphorus concentration inside the algae.

[0142] In addition, the consumption of carbon in plants comes from respiration and natural extinction. The intensity of respiration is limited by temperature, so there is a temperature limit function f for respiration r (T) The natural extinction process is caused by external environmental factors that are not suitable for algae growth, and is subject to the related functions of light radiation intensity, nutrient concentration and extreme temperature conditions, that is, it is limited by the photorespiration function f(I), the nutrient limitation function f(N) and the temperature limitation function f(N) of the natural extinction process. m (T). When the external environment temperature is not suitable for algae survival, the temperature limit function f of respiration r (T) remains at a low value, the respiration intensity drops to the lowest level, and the natural extinction process dominates the biological process of algae.

[0143] Then the expression of the carbon consumption rate inside the algae is:

[0144] L c =R d f r (T)·C+f(I)f m (T)f(N)·C (14)

[0145] In the formula, R d represents the dark respiration rate.

[0146] Similar to the carbon consumption process in plants, the consumption of nitrogen and phosphorus in plants is highly correlated with respiration, and the following expression is available:

[0147] L DIN =R d Q N f r (T)·C (15)

[0148] L DIP =R d Q P f r (T)·C (16)

[0149] Where, L DIN Represents the consumption rate of DIN inside the algae; L DIP represents the consumption rate of DIP inside the algae.

[0150] In some optional embodiments, based on the calculation of the absorption and consumption of carbon (C), nitrogen (N) and phosphorus (P), the plant volume (leaf area) method is used for calculation, which is suitable for large algae that need to consider the plant volume factor. This calculation method divides the carbon element C in the plant body into structural carbon C STRU and reserve carbon C RES . Then we have the following expression:

[0151] C=C STRU +C RES (17)

[0152] Among them, leaf area A is proportional to structural carbon, qAS is the ratio coefficient of leaf area to structural carbon, which is determined by plant characteristics and is expressed as follows:

[0153] A=qAS·C STRU (18)

[0154] In addition, structural carbon is converted from reserve carbon through structural growth, Gro is the structural carbon growth, and the leaf loss rate and erosion rate are set. Among them, the structural carbon loss amount Ero caused by leaf necrosis loss and the structural carbon loss amount Nec caused by leaf erosion are related to light intensity, temperature and water flow rate. Then the structural carbon V STRU The expression of the rate of change of content with time t is as follows:

[0155]

[0156] Gro=μ max ·f(A)·f(T)·f(Q)·C STRU (20)

[0157] In the formula, μ max is the maximum structural growth rate, which is limited by leaf area, light intensity, temperature and reserve nitrogen; f(A) is the leaf area limitation function, which represents the correlation between the leaf area of ​​the alga and the structural growth rate; f(T) is the temperature limitation function of structural growth, which represents the correlation between the external temperature and the structural growth rate, which is determined by the physiological characteristics of the alga; f(Q) is the plant reserve carbon and reserve nitrogen content limitation function of structural growth, which represents the correlation between the internal reserve carbon and nitrogen indicators of the alga and the structural growth rate.

[0158] Among them, the change of leaf area affects the growth rate of plant structure, A 0 is the growth limiting parameter of plant growth scale. When the leaf area is close to A 0 When the growth rate of the plant structure slows down, the following expression is obtained:

[0159] f(A)=m 1 ·exp(-(A / A 0 ) 2 ) (twenty one)

[0160] In the formula, m 1 It is an intermediate parameter of the effect of plant growth scale on plant growth rate, which is determined by changes in leaf area.

[0161] The reserve carbon and reserve nitrogen indexes in the plant body affect the growth rate of the plant structure. The expression of the limit function f(Q) of the reserve carbon and reserve nitrogen content of the plant body for structural growth is as follows:

[0162]

[0163] In the formula, qRS represents the reserve carbon quota corresponding to the unit structural carbon, that is, the ratio of the current reserve carbon to the structural carbon in the plant body, C RES :C STRU , qRS minis the minimum reserve carbon ratio; qNS represents the reserve nitrogen quota corresponding to the unit structural carbon, that is, the ratio of the current nitrogen content in the plant to the structural carbon N:C STRU , qNS min This is the minimum reserve nitrogen ratio.

[0164] In this calculation example, the reserve carbon C RES The calculation of is consistent with formula (4), that is, The reserve carbon absorption rate V c Same as formula (9), in addition, the calculation of reserve carbon loss includes the transfer and loss of reserve carbon caused by respiration, structural growth, plant structure necrosis and structural erosion. The loss of plant structure is accompanied by the loss of structural carbon and reserve carbon of the corresponding quota. The loss of reserve carbon is calculated as follows:

[0165] L CRES =R d f r (T)·C-Gro-qRS(Ero+Nec) (23)

[0166] Where, L CRES represents the loss of reserve carbon; R d represents the dark respiration rate; f r (T) represents the temperature limiting function of respiration.

[0167] In step S400 of some embodiments, the short-term prediction results are post-processed into a publishable forecast product to obtain an algae forecast result. Exemplarily, the post-processing method may include but is not limited to statistical correction, spatial and temporal smoothing, noise filtering, and error adjustment.

[0168] For example, in the FVCOM hydrodynamic forecast model, FVCOM (Finite-Volume Coastal Ocean model) is a comprehensive ocean model that integrates modules such as hydrodynamics, sediment transport and landform evolution, water quality, ecology, and sea ice. This model uses an unstructured grid and solves the three-dimensional primitive equations of the ocean based on the finite volume algorithm. The three-dimensional primitive equations of the ocean include the momentum equation, the mass continuity equation, the temperature and salinity diffusion equation, and the density equation. And this model closes the above three-dimensional primitive equations of the ocean through the Mellor-Yamada 2.5-order vertical turbulence closure model and the Smagorinsky horizontal turbulence closure model. This model uses the σ coordinate system or the general vertical s coordinate system to fit the irregular bottom terrain in the vertical direction, and uses non-overlapping unstructured triangular grids to spatially discretize the calculation area horizontally, such as Figure 5As shown in the figure, this method can fit the coastline well and facilitate local grid encryption. Compared with the structured grid, the coastline model of the unstructured grid fits the real coastline better.

[0169] In some embodiments, FVCOM solves the integral form of the control equation to obtain the flux between triangular control bodies in a horizontal non-overlapping unstructured triangular grid. Exemplarily, it can be solved by a finite volume discretization method (FVM), which has the flexibility of the finite element method (FEM) in geometric topology and the simple discrete structure and efficient calculation of the finite difference method (FDM). At the same time, the method can also better ensure the conservation of mass, momentum, salinity and heat in estuary and coastal areas with complex terrain and in ocean calculations. In addition, the above-mentioned comprehensive ocean model can use dry and wet moving boundary technology to deal with the dynamic flooding process of tidal flats.

[0170] In some embodiments, the FVCOM control equations can be converted from Cartesian coordinates to terrain tracking coordinates. Due to the irregular changes in the seabed topography, taking a simple z-coordinate will result in many holes on the seabed, which increases the difficulty of dealing with the seabed boundary. The σ-coordinate can fit the bottom topography well, but in deep water areas, the grid spacing on the surface mixed layer and the bottom boundary layer is relatively large, so the physical characteristics of these special areas are difficult to show. The variable σ layering, that is, the s-coordinate, is an approximate z-σ mixed coordinate that can ensure a finer vertical resolution in certain special areas such as the surface or bottom layer on the basis of fitting the terrain, but it requires a certain water depth to establish. Generally, a general function can be established. To define the terrain tracking coordinate system, x and y represent the east and north coordinates respectively, and r is the vertical coordinate that varies between -1 and 0. r can be the above-mentioned σ, s or any other function that satisfies the conditions to describe the vertical coordinate changes. Figure 6 As shown, Figure 6 is a schematic diagram of σ-s mixed coordinates. Therefore, the FVCOM control equations can be converted from Cartesian coordinates to terrain tracking coordinates, and the calculation process is as follows:

[0171]

[0172]

[0173] ρ=ρ(T,S,p) (24-7)

[0174] Where x, y and z represent the east, north and vertical coordinates in the coordinate system respectively; u, v and w are the velocity components in the x, y and z directions in the Cartesian coordinate system respectively; T represents temperature; S represents salinity; ρ represents water density; P a is the sea surface pressure; f is the Coriolis parameter; g is the gravitational acceleration; K m is the vertical eddy viscosity coefficient; K h is the thermal vertical eddy diffusion coefficient; F u 、F v is the horizontal momentum diffusion term; F T and F s are the diffusion terms of temperature and salinity, respectively. A 1 and A 2 is the coordinate transformation coefficient,

[0175] Among them, the converted vertical velocity satisfies the continuity equation:

[0176]

[0177] And ω has the following relationship with the actual vertical flow velocity w:

[0178]

[0179] In some embodiments, similar to the finite element method, the finite volume algorithm also divides the calculation area into non-overlapping triangular elements, and the configuration of the variables in the triangular elements is as follows: Figure 7 As shown, Figure 7 Each solid black dot in the figure is a node. is the centroid, (u,v) is a vector. Let the centroid and total number of nodes of the triangles in the calculation area be N and M respectively. Then the coordinates of the centroid can be expressed as:

[0180] [X(i),Y(i)],i=1:N (24-10)

[0181] Similarly, the coordinates of the nodes can be expressed as:

[0182] [X n (j),Y n (j)],j=1:M (24-11)

[0183] Since the triangles do not overlap, N also represents the number of triangular meshes. In order to more accurately estimate the water level, flow velocity and temperature-salinity flux, FVCOM places u and v on the triangle centroids for calculation (the number of centroids is approximately twice the number of nodes), while other scalars are placed on the nodes for calculation, such as the free surface height ζ, the bottom depth H, the overall water column depth D, ω, salinity S, temperature T, water density ρ, and the vertical eddy viscosity coefficient K. m , thermal vertical eddy diffusion coefficient K h , horizontal rotation coefficient A m and thermal diffusion coefficient A h The value of each variable at the node is calculated as follows Figure 7 The net flux in the yellow area is obtained by the calculation of the net flux in the yellow area. This area is called the tracking control element (TCE), and each side of it is the line connecting the centroid of the triangle and the midpoint of the edge. The u and v at the centroid of the triangle are determined by calculating the net flux through the three sides of the triangle. A triangular mesh is also called a momentum control element (MCE). Similar to finite difference models such as POM and ROMS, FVCOM also removes ω and turbulent variables (such as q 2 and q 2 l) are placed in the middle of each σ layer for calculation. Since there is no requirement on the thickness of the σ layer, non-uniform σ coordinates can also be applied.

[0184] In some embodiments, FVCOM assumes that the bottom boundary layer of the flow satisfies the classical logarithmic profile:

[0185]

[0186] When only flow is applied, the bottom friction stress adopts the form of quadratic parameterization:

[0187]

[0188] Where u(z) represents the flow velocity at a height z from the seafloor; u * represents the friction velocity, also known as shear velocity, which is the shear stress expressed in velocity units; κ 1 represents the von Karman constant; z 0 represents the bottom roughness; τ c represents the bottom friction stress generated by the flow; C d represents the drag coefficient; represents the bottom flow velocity vector; u *c represents the friction velocity generated by the flow. Therefore, the drag coefficient C d The expression is:

[0189]

[0190] In the formula, κ 1represents the von Karman constant, κ 1 =0.4; z ab is the reference height. In FVCOM, the reference height z ab The height of the bottom layer.

[0191] The embodiment of the present invention also provides a large floating algae short-term forecasting system, which can implement the above-mentioned large floating algae short-term forecasting method, such as Figure 2 As shown in FIG. 1 , the system includes: the FVCOM (Finite-Volume Coastal Ocean model) regional ocean hydrodynamic prediction model (i.e., the FVCOM hydrodynamic prediction model) and the large floating algae growth and drift model based on Lagrangian particles. After the remote sensing monitoring results are input into the prediction system, the prediction system automatically runs, initializes the prediction system through the real-time monitoring data of remote sensing monitoring, starts the prediction calculation, and finally realizes the 7-day short-term prediction of the biomass, coverage, transportation trajectory, stranding location and biomass of large algal blooms, and post-processes the short-term prediction results into a publishable forecast product, realizing a complete forecast process.

[0192] For example, taking the operational forecast and early warning work of the green tide outbreak disaster in the South Yellow Sea of ​​China from 2022 to 2024 as an example, the following steps are included:

[0193] 1) Construction of the East China Sea Sea Dynamics Forecast Model

[0194] 1.1) Hydrodynamic forecast model architecture

[0195] like Figure 8 As shown in the figure, the Jiangsu Basin Model is divided into hindcast and forecast models. By considering factors such as tides, circulation, wind field, and sea surface heat flux, a complete hindcast model is constructed by combining data assimilation and downscaling techniques, and then the model parameters are optimized and corrected through astronomical tide data and measured data. On the basis of the hindcast model, the GFS forecast wind field is used instead of the ECMWF reanalysis data to establish an automated hydrodynamic forecast model.

[0196] 1.2) Calculation area & grid construction

[0197] like Fig. 9 As shown in the figure, the model calculation area covers the Yellow Sea, Bohai Sea, East China Sea and Sea of ​​Japan, with the northernmost and southernmost latitudes reaching 42°N and 20.5°N respectively, and the easternmost longitude reaching 143°E. The open boundary extends outward from the Taiwan Strait, including the Kuroshio path in the East China Sea shelf area, with the deepest water depth reaching 7500m. The core calculation area is the coastal waters of Jiangsu.

[0198] For complex terrain such as the Subei Shoal, it is difficult for conventional manually made grids to accurately depict its terrain. This time, the model uses the Smeshing adaptive grid generation method and generates grids with program iteration to ensure the quality of the grid. At the same time, the corresponding relationship between the water depth gradient and the grid resolution is artificially established. The water depth data is first interpolated into the triangular grid, and then the gradient is calculated based on the grid water depth. Finally, given the relationship between the water depth gradient and the resolution, the grid resolution is high where the water depth gradient is large, and the grid resolution is low where the water depth gradient is small, so as to generate a water depth gradient resolution matrix. Because the overall grid quality generated by the water depth gradient resolution matrix is ​​difficult to control, we only apply this method in the Subei Shoal area, and continue to use the resolution matrix generated before the iteration in other areas. The results are shown below. Fig.10 The advantage of this method is that it can accurately depict the terrain of the Subei shoal, control the number and quality of grids and improve the calculation efficiency.

[0199] The grid of Subei Shoal area is as follows: Fig.11 As shown in the figure, setting the resolution of the interior and shoreline areas of the Subei Shoal to 1.5 km enables the model to simulate the dynamic process more accurately. The model combines the resolution of the grid with the water depth gradient, so that the complex terrain inside the Subei Shoal is more accurately portrayed, and the quality and quantity of the grid can be effectively controlled, laying a solid and reliable foundation for the next step of the dynamic process.

[0200] 1.3) Data Source

[0201] The water depth data uses the ETOP1 ice surface model data, which is obtained by integrating the measurement data of multiple institutions at the United States Geophysical Center. The data covers the entire world with a resolution of 1 arc minute. It is replaced by measured terrain data in the coastal area of ​​Jiangsu. The physical field data uses HYCOM+NCODA Global 1 / 12° analysis data (GLBy0.08), which comes from a near real-time global ocean forecast system, including temperature, salinity, current velocity, sea surface height, mixed layer thickness, sea surface heat flux and water flux. Meteorological data is obtained through ECMWF, including 10m wind speed at the sea surface, long and short wave radiation, latent heat, and sensible heat, with a time interval of six hours. The astronomical tide data comes from the TPXO7.2 ocean model. Eight main tides are used in this model, namely M 2 , S 2 , K 2 、N 2 , K 1 , O 1 , Q 1 , P 1The sea surface temperature assimilation and current velocity assimilation data are derived from MEaSUREs (Multi-scale Ultra-high Resolution SST) and ground wave radar observations, respectively. The river input is derived from the Datong station data. In the forecast model, the atmospheric forcing is derived from the three-hourly GFS forecast wind field.

[0202] 1.4) Model refinement settings

[0203] 1.4.1) Solution and parameter setting

[0204] The solution in FVCOM supports the internal and external model method and the semi-implicit solution. The internal and external model method divides the model into the internal model and the external model. Usually, the external model calculates the simple barotropic process, while the internal model calculates the more complex baroclinic process. The time steps of the internal and external models are inconsistent. Adjustments are made at each internal model time step to ensure the consistency of the internal and external model results. The internal and external model times are unified through interpretive codes. This internal and external model method significantly improves the efficiency of model calculation. The semi-implicit solution contains the (n+1)th time term, so it requires iterative solution. The calculation efficiency is lower than that of the internal and external model method, but the semi-implicit solution has more accurate calculation results, especially in the vertical water transport process. In this model, the semi-implicit solution is used, and the time increment is 30s. It is divided into 20 sigma layers in the vertical direction. Considering the heat flux of the air-sea interface and the fitting of the terrain, two 10m layers are set at the surface bottom layer at a water depth greater than 200m. The spatial distribution of bottom friction is obtained through 35 tidal simulation iterations.

[0205] 1.4.2) Initial Field Setup

[0206] The setting of initial conditions has a great influence on the solution of equations, especially in the area of ​​nearshore mixed freshwater and saltwater. The physical initial field of this model is obtained by interpolation on the grid using HYCOM data, and the temperature and salinity data are interpolated to 40 standard layers. However, some HYCOM data near the coast are missing, and it is necessary to obtain the correct initial field by methods other than interpolation. In the hindcast model, the model can achieve a more accurate temperature-salinity field by setting a sufficient time advance before the target simulation time. In the forecast model, the problem of the initial temperature-salinity field can be solved by hot start.

[0207] 1.4.3) Boundary settings

[0208] Grid refinement techniques such as nesting and adaptive grids can be used to give the ocean model the ability to change resolution, so that the model can better solve problems of different scales. This model uses the nesting technique to set the boundary conditions of the model. This technique can transfer mass and momentum into the region and has good conservation on unstructured grids. The nested boundary is Fig. 9The nested boundary starts from the Taiwan Strait and ends in the Sea of ​​Japan, extending inward by 4 layers of units, with a total of 1814 nodes. The vertical velocity, temperature, and salinity data of HYCOM are interpolated to the nested area. The velocity data is a combination of the HYCOM velocity and the TPOX tidal velocity. The water level data is a combination of the sea surface height and the tidal water level data, which are used to drive the circulation of the entire area. The calculation method is as follows:

[0209] U=U H +U T (25-1)

[0210] V=V H +V T (25-2)

[0211] ζ=SSH+ζ T (25-3)

[0212] Where U is the component of the velocity in the east-west direction, V is the component of the velocity in the north-south direction, ζ is the water level, and U H 、V H is the HYCOM velocity data component, SSH is the HYCOM sea surface height data, U T 、V T is the tidal velocity component, ζ T It is the tidal water level data, which is obtained by the Matlab package T_tide according to the TPXO7.2 harmonic constant forecast.

[0213] 1.4.4) Atmospheric forcing

[0214] Atmospheric forcing includes wind field and sea surface heat flux, both of which are derived from ECMWF models and observational reanalysis data. The wind field uses the north-south wind speed at a height of 10m, and the heat flux is obtained by calculating net solar radiation, net thermal radiation, latent heat, and sensible heat.

[0215] 2) Setting up the Yellow Sea Enteromorpha growth and drift model

[0216] Based on the physiological characteristics of Enteromorpha in the Yellow Sea, the empirical formulas of various temperature limit functions are as follows:

[0217]

[0218] According to the floating growth characteristics of Enteromorpha in the Yellow Sea, when the algae aggregate growth density exceeds 0.16 molC / m 2 , the growth process of algae begins to be restricted by light shading, and the empirical formula of the light shading restriction function is set as:

[0219]

[0220] For the simulation of Enteromorpha in the Yellow Sea, the parameters in the growth module are set as shown in Table 1 based on the physiological research results of Enteromorpha in the Yellow Sea.

[0221] Table 1

[0222] parameter describe Numeric unit <![CDATA[Q Nmin ]]> Minimum nitrogen-carbon ratio (nitrogen concentration index) 25.3 <![CDATA[mmol N mil C -1 ]]> <![CDATA[Q Nmax ]]> Maximum nitrogen-carbon ratio (nitrogen concentration index) 108.7 <![CDATA[mmol N mol C -1 ]]> <![CDATA[Q Pmin ]]> Minimum phosphorus-carbon ratio (phosphorus concentration index) 0.097 <![CDATA[mmol N mil C -1 ]]> <![CDATA[Q Pmax ]]> Maximum phosphorus-carbon ratio (phosphorus concentration index) 1.4 <![CDATA[mmol N mil C -1 ]]> <![CDATA[V mDIN ]]> Maximum DIN absorption rate 2.8 <![CDATA[mmol N mol C -1 h -1 ]]> <![CDATA[V mDIp ]]> Maximum DIP absorption rate 0.58 <![CDATA[mmol N mil C -1 h -1 ]]> <![CDATA[K DIN ]]> DIN half-saturation coefficient 18.77 <![CDATA[μmol N L -1 ]]> <![CDATA[K DIP ]]> DIP half saturation coefficient 10 <![CDATA[μmol N L -1 ]]> <![CDATA[P max ]]> Maximum photosynthesis rate 240.51 <![CDATA[μmol C g WW -1 h -1 ]]> α Photosynthesis efficiency 2.52 <![CDATA[(μmol C g WW -1 h -1 ) / (μmolphotons m -2 s -1 )]]> <![CDATA[R d ]]> Dark respiration rate 18.4 <![CDATA[μmol C g WW -1 h -1 ]]>

[0223] 3) Presentation of the short-term forecast results of the Yellow Sea green tide in 2022

[0224] like Fig.12a As shown, Fig.12a It is the short-term forecast product of the Yellow Sea green tide from May 31, 2022 to June 2, 2022; Figure 12b As shown, Figure 12b It is the short-term forecast product of the Yellow Sea green tide from June 2, 2022 to June 7, 2022; as shown in Table 2 below, Table 2 is an example of the 2022 Yellow Sea green tide short-term forecast report.

[0225] In addition, according to the short-term forecast results for 2022 and 2023, in the 7-day forecast results, the forecast accuracy of the forecast system of the embodiment of the present invention for the coverage area can reach up to 87.5%, and the minimum error for the transportation path is only 6.09 nautical miles.

[0226] Table 2

[0227]

[0228] The embodiment of the present invention further provides a large floating algae short-term forecasting device, which can implement the above-mentioned large floating algae short-term forecasting method, and the device includes:

[0229] The first module is used to input the physical field data into the FVCOM hydrodynamic prediction model to obtain the hydrodynamic model prediction results;

[0230] The second module is used to obtain the remote sensing monitoring results of the day through remote sensing monitoring;

[0231] The third module is used to input the hydrodynamic model prediction results and the remote sensing monitoring results into the large floating algae growth and drift model to obtain short-term prediction results;

[0232] The fourth module is used to post-process the short-term prediction results to obtain algae forecast results.

[0233] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0234] The embodiment of the present invention further provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and the processor implements the above-mentioned large floating algae short-term forecasting method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0235] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0236] refer to Fig.13 , Fig.13 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0237] The processor 501 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0238] The memory 502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 502, and the processor 501 is called to execute a large floating algae short-term forecasting method of the embodiment of the present invention;

[0239] Input / output interface 503, used to implement information input and output;

[0240] Communication interface 504, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0241] A bus 505 that transmits information between the various components of the device (e.g., the processor 501, the memory 502, the input / output interface 503, and the communication interface 504);

[0242] The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via the bus 505 .

[0243] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for short-term forecasting of large floating algae is implemented.

[0244] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0245] The embodiment of the present invention also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aforementioned large floating algae short-term forecasting method.

[0246] In summary, the large floating algae short-term forecasting method and device according to the embodiment of the present invention has the following advantages:

[0247] 1. The embodiment of the present invention solves the technical problems in the forecast of algae disasters, such as the simple forecast model of algae outbreak scale, the difficulty in quantifying the forecast of algae outbreak biomass and coverage, and the insufficient reliability of algae transport trajectory, by combining the particle tracing model with the algae growth model, with the support of regional marine environment and climate state forecast results and remote sensing monitoring data.

[0248] 2. The physical-ecological dynamics short-term operational forecasting system for large floating algae established in the embodiment of the present invention is based on the Lagrangian particle tracing method, taking into account the effects of environmental factors such as temperature, light, salinity, and nutrients on the growth and extinction of algae. The simulation and prediction method better reproduces the physical-biological process of algae disaster outbreaks.

[0249] 3. The embodiment of the present invention realizes short-term prediction of the outbreak scale, impact area and biomass of large floating algae outbreaks by combining with real-time remote sensing monitoring data, thus meeting the business needs of green tide forecasting and early warning.

[0250] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0251] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0252] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0253] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0254] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0255] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0256] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0257] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0258] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A short-term forecasting method for large floating algae, characterized in that: The following steps are involved: Input the physical field data into the FVCOM hydrodynamic prediction model to obtain the hydrodynamic pattern prediction results; Obtain the remote sensing monitoring results of the day through remote sensing monitoring; Inputting the hydrodynamic model prediction results and the remote sensing monitoring results into a large floating algae growth and drift model to obtain a short-term prediction result; The short-term prediction results are post-processed to obtain algae forecast results.

2. A short-term forecasting method for large floating algae according to claim 1, characterized in that: The method of inputting the hydrodynamic model prediction results and the remote sensing monitoring results into a large floating algae growth and drift model to obtain a short-term prediction result includes the following steps: Based on the prediction results of the hydrodynamic model, Lagrangian particle tracing is performed through a particle tracing module to obtain the physical transport process; According to the hydrodynamic model prediction results and the remote sensing monitoring results, the algae growth module is used to replicate and merge particles to obtain a dynamic growth process; coupling the physical transport process and the dynamic growth process to obtain a short-term prediction result; The large floating algae growth and drift model includes the particle tracing module and the algae growth module.

3. A short-term forecasting method for large floating algae according to claim 2, characterized in that: The method of performing Lagrangian particle tracing using a particle tracing module based on the hydrodynamic model prediction results to obtain the physical transport process includes the following steps: Performing temporal interpolation and spatial interpolation operations on the hydrodynamic model prediction results to obtain ocean current velocity; Obtain sea surface wind forcing through meteorological forecast models; Set the wind drag coefficient according to the floating characteristics; According to the unit motion speed in the random direction, the random diffusion coefficient in the horizontal direction and the time step, the random motion in the horizontal direction is obtained; Based on the fourth-order Runge-Kutta method, the Lagrangian particle drift trajectory is obtained according to the ocean current velocity, the sea surface wind forcing, the wind drag coefficient, and the horizontal random motion; The ocean currents and sea surface winds in the hydrodynamic model prediction results are used as physical driving forces of the particles; The physical transport process is obtained according to the physical drive and the Lagrangian particle drift trajectory.

4. A short-term forecasting method for large floating algae according to claim 3, characterized in that: The expression of the Lagrangian particle drift trajectory is: Where, X t+Δt represents the horizontal position of the particle at time t+Δt; X t represents the horizontal position of the particle at time t; represents particle x at time t t Velocity vector in the horizontal direction; Represents the random motion of particles in the horizontal direction within the time step Δt of particle physics motion calculation; x t Represents particles; t represents time; Δt represents the time step of particle physics motion calculation.

5. The method for short-term forecasting of large floating algae according to claim 2, characterized in that: The method of replicating and merging particles using an algae growth module based on the hydrodynamic model prediction results and the remote sensing monitoring results to obtain a dynamic growth process includes the following steps: Preset initial biomass; Get the current first biomass of a single particle; replicating the single particle according to the current first biomass of the single particle and the initial biomass; merging the single particle and the neighboring particles according to the current first biomass of the single particle, the initial biomass, and the current second biomass of neighboring particles of the single particle; According to the operations of copying and merging, the dynamic growth process is obtained.

6. A short-term forecasting method for large floating algae according to claim 5, characterized in that: The replicating of the single particle according to the current first biomass of the single particle and the initial biomass comprises the following steps: When the current first biomass of the single particle is greater than twice the initial biomass, a new particle is randomly copied and placed within a preset range of the single particle, and the current first biomass of the single particle and the current third biomass of the new particle are both set to the initial biomass.

7. The method for short-term forecasting of large floating algae according to claim 5, characterized in that: The merging of the single particle and the neighboring particles according to the current first biomass of the single particle, the initial biomass, and the current second biomass of the neighboring particles of the single particle comprises the following steps: When the current first biomass of the single particle and the current second biomass of the neighboring particles of the single particle are both less than half of the initial biomass, the single particle and the neighboring particles are merged to obtain a merged particle, and the current fourth biomass of the merged particle is set as the initial biomass.

8. A short-term forecast device for large floating algae, characterized in that: include: The first module is used to input physical field data into the FVCOM hydrodynamic prediction model to obtain hydrodynamic model prediction results; The second module is used to obtain the remote sensing monitoring results of the day through remote sensing monitoring; The third module is used to input the hydrodynamic model prediction results and the remote sensing monitoring results into a large floating algae growth and drift model to obtain a short-term prediction result; The fourth module is used to post-process the short-term prediction results to obtain algae forecast results.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

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